Variation in caesarean delivery rates across hospitals: a Bayesian semi-parametric approach

M. Cannas*, C. Conversano, F. Mola, Emiliano Sironi

*Autore corrispondente per questo lavoro

Risultato della ricerca: Contributo in rivistaArticolo in rivista

1 Citazioni (Scopus)


This article presents a Bayesian semi-parametric approach for modeling the occurrence of cesarean sections using a sample of women delivering in 20 hospitals of Sardinia (Italy). A multilevel logistic regression has been fitted on the data using a Dirichlet process prior for modeling the random-effects distribution of the unobserved factors at the hospital level. Using the estimated random effects at the hospital level, a partition of the hospitals in terms of similar medical practice has been obtained that identifies different profiles of hospitals in terms of caesarean section risks. The limited number of clusters may be useful for suggesting policy implications that help to reduce the heterogeneity of caesarean delivery risks.
Lingua originaleEnglish
pagine (da-a)2095-2107
Numero di pagine13
RivistaJournal of Applied Statistics
Stato di pubblicazionePubblicato - 2017


  • Caesarean sections
  • Dirichlet process
  • label-switching problem
  • random effect models
  • random partition


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